Keyword searches performed by conventional search applications (e.g., Internet search engines, program guide applications, desktop search applications, etc.) often produce a plethora of results, many of which are of little or no interest to a user. Consequently, the user may attempt to filter the results by combining additional keywords with the originally input keywords until a satisfactory list of results is generated.
Unfortunately, it is often difficult for a user to ascertain how related each search result is to each of the various keywords used to perform a search. For example, if first and second keywords are used to perform a particular search, it is often difficult for a user to know which of the results are more related to the first keyword, which of the results are more related to the second keyword, and which of the results share the most attributes with both the first and second keywords. Consequently, the user may waste time and resources combing through the results in order to identify results that are of most interest to the user.